Carbon instensity of Sustainable PV for Energy Transition¶

This analysis explores the carbon implications of different PV sustainability/circular economy designs in the context of achieving energy transition. These calculations build upon previous work that can be found in journals 13 and 17.

Attempt 1

In [1]:
import numpy as np
import pandas as pd
import os,sys
from pathlib import Path
import matplotlib.pyplot as plt

cwd = os.getcwd() #grabs current working directory

testfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP' / 'CarbonAnalysis')
inputfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP')
baselinesfolder = str(Path().resolve().parent.parent /'PV_ICE' / 'baselines')
supportMatfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'SupportingMaterial')
carbonfolder = str(Path().resolve().parent.parent / 'PV_ICE'/ 'baselines'/ 'CarbonLayer')
altBaselinesfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'Energy_CellModuleTechCompare')
energyanalysisfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP' / 'EnergyAnalysis')

if not os.path.exists(testfolder):
    os.makedirs(testfolder)
In [2]:
from platform import python_version 
print(python_version())

3.8.8
In [3]:
import PV_ICE
PV_ICE.__version__
Out[3]:
'v0.2.0+520.g4b320f8.dirty'
In [4]:
#https://www.learnui.design/tools/data-color-picker.html#palette
#color pallette - modify here for all graphs below
colorpalette=['#000000', #PV ICE baseline
              '#595959', '#7F7F7F', '#A6A6A6', '#D9D9D9', #BAU, 4 grays, perc, shj, topcon, irena
              #'#067872','#0aa39e','#09d0cd','#00ffff', #realistic cases (4) teals, perc, shj, topcon, irena
              '#0579C1','#C00000','#FFC000', #extreme cases (3) long life, high eff, circular
                '#6E30A0','#00B3B5','#10C483', #ambitious modules (5) high eff+ long life, 50 yr perc, recycleSi, 
               '#97CB3F','#FF7E00' #circular perovskite+life, circular perovkiste+ high eff
                ] 

colormats = ['#00bfbf','#ff7f0e','#1f77be','#2ca02c','#d62728','#9467BD','#8C564B', 'black'] #colors for material plots       

import matplotlib as mpl #import matplotlib
from cycler import cycler #import cycler
mpl.rcParams['axes.prop_cycle'] = cycler(color=colorpalette) #reset the default color palette of mpl

plt.rcParams.update({'font.size': 14})
plt.rcParams['figure.figsize'] = (8, 6)

scennames_labels = ['PV_ICE','PERC','SHJ','TOPCon','Low\nQuality',
                         'Long-Lived','High Eff','Circular',
                        'High Eff\n+ Long-life','Long-Life\n+ Recycling',
                         'Recycled-Si\n+ Long-life','Circular\n+ Long-life',
                        'Circular\n+ High Eff'
                    ]  

scennames_labels_flat = ['PV_ICE','PERC','SHJ','TOPCon','Low Quality',
                         'Long-Lived','High Eff','Circular',
                        'High Eff + Long-life','Long-Life + Recycling',
                         'Recycled-Si + Long-life','Circular + Long-life',
                        'Circular + High Eff'
                    ]
In [5]:
MATERIALS = ['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet']
moduleFile_m = os.path.join(baselinesfolder, 'baseline_modules_mass_US.csv')
moduleFile_e = os.path.join(baselinesfolder, 'baseline_modules_energy.csv')
In [6]:
#load in the simulation from Energy Analysis journal
sim1 = PV_ICE.Simulation.load_Simpickle(filename=r'C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\TEMP\EnergyAnalysis\sim1.pkl')

sim1.calculateCarbonFlows()

sim1.scenario['r_PERC'].dataOut_c

In [7]:
sim1.scenario['r_PERC'].dataOut_m
Out[7]:
Area Cumulative_Active_Area EOL_BadStatus EOL_Landfill0 EOL_PATHS EOL_PG Effective_Capacity_[W] Landfill_0_ProjLife MerchantTail_Area MerchantTail_[W] ... Yearly_Sum_Area_PathsBad Yearly_Sum_Area_PathsGood Yearly_Sum_Area_atEOL Yearly_Sum_Power_EOLby_Degradation Yearly_Sum_Power_EOLby_Failure Yearly_Sum_Power_EOLby_ProjectLifetime Yearly_Sum_Power_PathsBad Yearly_Sum_Power_PathsGood Yearly_Sum_Power_atEOL irradiance_stc
0 5.237421e+06 5.237421e+06 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 7.520622e+08 0.000000e+00 0.0 0.0 ... 0.000000e+00 0.000000e+00 0.000000e+00 0.0 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1070.0
1 2.381558e+06 7.618978e+06 5.019410e-03 2.844333e-02 5.019410e-03 0.000000e+00 1.092986e+09 0.000000e+00 0.0 0.0 ... 3.346274e-02 0.000000e+00 3.346274e-02 0.0 4.769010e+00 0.000000e+00 4.769010e+00 0.000000e+00 4.769010e+00 1070.0
2 2.399211e+06 1.001819e+07 3.883317e-01 2.200546e+00 3.883317e-01 0.000000e+00 1.437059e+09 0.000000e+00 0.0 0.0 ... 2.588878e+00 0.000000e+00 2.588878e+00 0.0 3.662374e+02 0.000000e+00 3.662374e+02 0.000000e+00 3.662374e+02 1070.0
3 3.636895e+06 1.365505e+07 4.784439e+00 2.711182e+01 4.784439e+00 0.000000e+00 1.965030e+09 0.000000e+00 0.0 0.0 ... 3.189626e+01 0.000000e+00 3.189626e+01 0.0 4.481373e+03 0.000000e+00 4.481373e+03 0.000000e+00 4.481373e+03 1070.0
4 7.372109e+06 2.102697e+07 2.774546e+01 1.572243e+02 2.774546e+01 0.000000e+00 3.051979e+09 0.000000e+00 0.0 0.0 ... 1.849697e+02 0.000000e+00 1.849697e+02 0.0 2.582007e+04 0.000000e+00 2.582007e+04 0.000000e+00 2.582007e+04 1070.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
96 7.591823e+09 3.541404e+11 1.667387e+08 1.552650e+09 4.657949e+09 4.491210e+09 8.508872e+13 1.497070e+09 0.0 0.0 ... 2.223183e+08 4.491210e+09 6.210598e+09 0.0 4.896471e+10 1.297565e+12 4.896471e+10 9.731736e+11 1.346529e+12 1070.0
97 8.003568e+09 3.554404e+11 1.278019e+08 1.675878e+09 5.027633e+09 4.899831e+09 8.530804e+13 1.633277e+09 0.0 0.0 ... 1.704025e+08 4.899831e+09 6.703511e+09 0.0 3.775151e+10 1.415620e+12 3.775151e+10 1.061715e+12 1.453372e+12 1070.0
98 4.560406e+09 3.574687e+11 1.455385e+08 6.330470e+08 1.899141e+09 1.753602e+09 8.552736e+13 5.845342e+08 0.0 0.0 ... 1.940513e+08 1.753602e+09 2.532188e+09 0.0 4.301322e+10 5.066369e+11 4.301322e+10 3.799777e+11 5.496501e+11 1070.0
99 4.619609e+09 3.594903e+11 1.708910e+08 6.494993e+08 1.948498e+09 1.777607e+09 8.574668e+13 5.925356e+08 0.0 0.0 ... 2.278547e+08 1.777607e+09 2.597997e+09 0.0 5.050157e+10 5.135720e+11 5.050157e+10 3.851790e+11 5.640736e+11 1070.0
100 4.700811e+09 3.615007e+11 2.067210e+08 6.725818e+08 2.017745e+09 1.811024e+09 8.596600e+13 6.036748e+08 0.0 0.0 ... 2.756280e+08 1.811024e+09 2.690327e+09 0.0 6.103742e+10 5.232268e+11 6.103742e+10 3.924201e+11 5.842642e+11 1070.0

101 rows × 45 columns

Project grid forward to 100% re in 2050¶

To parallel the PV deployment, we will assume that we globally hit 100% RE in 2050 with the 75 TW of PV. As such, we need to change the future projection of marketshares of the different country grids.

One scenario with decarb grid, one scenario with decarb grid and heat

Estimating that 60-70% generation will be from Solar, 30-40% from wind, and any remainder from "other renewables"

In [8]:
countrygridmix = pd.read_csv(os.path.join(carbonfolder,'baseline_countrygridmix.csv'), index_col='year')
gridsources = ['Bioenergy','Hydro','Nuclear','OtherFossil','OtherRenewables','Solar','Wind']
nonRE = ['Coal','Gas','OtherFossil','Nuclear','Bioenergy']
In [9]:
countrygridmix.loc[2023:,:]=np.nan #delete 2023 to 2050
nonRE_search = '|'.join(nonRE) #create nonRE search
countrygridmix.loc[2050, countrygridmix.columns.str.contains(nonRE_search)] = 0.0 #set all nonRE to 0 in 2050
In [10]:
countrygridmix.loc[2050, countrygridmix.columns.str.contains('Solar')] = 63.0
countrygridmix.loc[2050, countrygridmix.columns.str.contains('Wind')] = 33.0
countrygridmix.loc[2050, countrygridmix.columns.str.contains('Hydro')] = 3.0
countrygridmix.loc[2050, countrygridmix.columns.str.contains('OtherRenewables')] = 1.0
#numbers derived from leading scenario electricity generation Breyer et al 2022 scenarios (EU focused)
In [11]:
countrygridmix_100RE2050 = countrygridmix.interpolate() #linearly interpolate between 2022 and 2050
In [12]:
apnd_idx = pd.RangeIndex(start=2051,stop=2101,step=1) #create temp df
apnd_df = pd.DataFrame(columns=countrygridmix_100RE2050.columns, index=apnd_idx )
countrygridmix_100RE20502100 = pd.concat([countrygridmix_100RE2050.loc[2000:],apnd_df], axis=0) #extend through 2100
countrygridmix_100RE20502100.ffill(inplace=True) #propogate 2050 values through 2100
In [13]:
countrygridmix_100RE20502100.loc[2050]
Out[13]:
China_Bioenergy            0.0
China_Coal                 0.0
China_Gas                  0.0
China_Hydro                3.0
China_Nuclear              0.0
                          ... 
Zambia_Nuclear             0.0
Zambia_OtherFossil         0.0
Zambia_OtherRenewables     1.0
Zambia_Solar              63.0
Zambia_Wind               33.0
Name: 2050, Length: 472, dtype: float64

This is a simple projection, assumes all countries have same ratio of PV and wind (which we know can't be true). Update in future with country specific projections.

In [14]:
pd.read_csv(os.path.join(carbonfolder,'baseline_electricityemissionfactors.csv'), index_col=[0])
Out[14]:
CO2eq_gpWh_IPCC2006 CO2eq_gpWh_ember CO2_gpWh_EIA CO2_gpWh_EPA
Energy Source
Bioenergy 0.3005 0.230 0.0000 0.3170
Coal 0.3487 0.820 0.3215 0.3380
Gas 0.2291 0.490 0.1805 0.1810
Hydro 0.0000 0.024 0.0000 0.0000
Nuclear 0.0000 0.012 0.0000 0.0000
OtherFossil 0.2671 0.700 0.2413 0.0000
OtherRenewables 0.0000 0.038 0.0000 0.0000
Solar 0.0000 0.048 0.0000 0.0000
Wind 0.0000 0.011 0.0000 0.0000
SteamAndHeat 0.0000 0.000 0.0000 0.2266
In [15]:
sim1.calculateCarbonFlows(countrygridmixes=countrygridmix_100RE20502100)


>>>> Calculating Carbon Flows <<<<

Working on Scenario:  PV_ICE
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  r_PERC
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  r_SHJ
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  r_TOPCon
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  r_IRENA
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  ex_Life
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  ex_High_eff
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  ex_Circular
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  h_EffLife
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  h_50PERC
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  h_RecycledPERC
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  h_Perovskite_life
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  h_Perovskite_Eff
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
In [ ]:

Carbon Analysis¶

this will become the aggregate carbon results function

In [16]:
scenarios = sim1.scenario
In [17]:
sim_carbon_results = pd.DataFrame()
sim_annual_carbon = pd.DataFrame()
for scen in scenarios:
    print(scen)
    mod_carbon_scen_results = sim1.scenario[scen].dataOut_c.add_prefix(str(scen+'_'))
    
    #mod annual carbon calcs here (selecting to avoid double counting)
    mod_mfg_carbon_total = mod_carbon_scen_results.filter(like='Global_gCO2eqpwh_mod_MFG_gCO2eq') #annual mfging carbon

    mod_nonvMFG = ['Install','OandM','Repair','Demount','Store','Resell','ReMFG','Recycle'] #could remove from loop
    nonvMFG_search = '|'.join(mod_nonvMFG) #create nonRE search
    mod_carbon_sum_nonvmfg = mod_carbon_scen_results.loc[:,mod_carbon_scen_results.columns.str.contains(nonvMFG_search)] #annual non mfging carbon
    scen_annual_carbon_mod = pd.concat([mod_mfg_carbon_total,mod_carbon_sum_nonvmfg], axis=1)
    scen_annual_carbon_mod[scen+'_Annual_Emit_mod_gCO2eq'] = scen_annual_carbon_mod.sum(axis=1)

    scenmatdc = pd.DataFrame()
    for mat in MATERIALS:
        print(mat)
        mat_carbon_scen_results = sim1.scenario[scen].material[mat].matdataOut_c.add_prefix(str(scen+'_'+mat+'_')) 
        
        #calculation for annual carbon emissions total (selecting to avoid double countings)
        mat_vmfg_total = mat_carbon_scen_results.filter(like='vMFG_total')
        mat_ce_recycle = mat_carbon_scen_results.filter(like='Recycle_e_p')
        mat_ce_remfg = mat_carbon_scen_results.filter(like='ReMFG_clean')
        mat_landfill = mat_carbon_scen_results.filter(like='landfill_total')
        mat_scen_annual_carbon = pd.concat([mat_vmfg_total,mat_ce_recycle,mat_ce_remfg,mat_landfill], axis=1)
        mat_scen_annual_carbon[scen+'_Annual_Emit_'+mat+'_gCO2eq'] = mat_scen_annual_carbon.sum(axis=1)
        
        scenmatdc = pd.concat([scenmatdc,mat_carbon_scen_results,
                               mat_scen_annual_carbon[scen+'_Annual_Emit_'+mat+'_gCO2eq']], axis=1) #group all material dc
    
    scen_carbon_results = pd.concat([mod_carbon_scen_results,scenmatdc], axis=1) #append mats to mod
    sim_carbon_results = pd.concat([sim_carbon_results, scen_carbon_results], axis=1) #append all scens "raw" data
    
    #calculate annual carbon emits with grouping by mod and mat
    scen_mats_annual_carbon = scenmatdc.filter(like='Annual_Emit')
    scen_mod_annual_carbon = scen_annual_carbon_mod.filter(like='Annual_Emit_mod')
    scen_annual_carbon = pd.concat([scen_mod_annual_carbon,scen_mats_annual_carbon], axis=1)
    scen_annual_carbon[scen+'_Annual_Emit_total_modmats_gCO2eq'] = scen_annual_carbon.sum(axis=1)
    sim_annual_carbon = pd.concat([sim_annual_carbon,scen_annual_carbon], axis=1)
    
    #FIX INDEX of dfs
sim_annual_carbon.index = pd.RangeIndex(start=2000,stop=2101,step=1)
sim_carbon_results.index = pd.RangeIndex(start=2000,stop=2101,step=1)
    
#return sim_carbon_results, sim_annual_carbon

PV_ICE
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
r_PERC
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
r_SHJ
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
r_TOPCon
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
r_IRENA
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
ex_Life
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
ex_High_eff
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
ex_Circular
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
h_EffLife
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
h_50PERC
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
h_RecycledPERC
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
h_Perovskite_life
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
h_Perovskite_Eff
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
In [18]:
sim_carbon_results
Out[18]:
PV_ICE_China_mod_MFG_gCO2eq PV_ICE_India_mod_MFG_gCO2eq PV_ICE_Taiwan_mod_MFG_gCO2eq PV_ICE_Germany_mod_MFG_gCO2eq PV_ICE_Japan_mod_MFG_gCO2eq PV_ICE_SKorea_mod_MFG_gCO2eq PV_ICE_Canada_mod_MFG_gCO2eq PV_ICE_Mexico_mod_MFG_gCO2eq PV_ICE_USA_mod_MFG_gCO2eq PV_ICE_VietNam_mod_MFG_gCO2eq ... h_Perovskite_Eff_backsheet_mat_LQeol_p_gCO2eq h_Perovskite_Eff_backsheet_mat_LQ_p_gCO2eq h_Perovskite_Eff_backsheet_mat_HQmfg_p_gCO2eq h_Perovskite_Eff_backsheet_mat_HQeol_p_gCO2eq h_Perovskite_Eff_backsheet_mat_HQ_p_gCO2eq h_Perovskite_Eff_backsheet_mat_vMFG_energy_gCO2eq h_Perovskite_Eff_backsheet_mat_vMFG_total_gCO2eq h_Perovskite_Eff_backsheet_mat_Recycle_e_p_gCO2eq h_Perovskite_Eff_backsheet_mat_landfill_total_gCO2eq h_Perovskite_Eff_Annual_Emit_backsheet_gCO2eq
2000 6.287490e+09 3.726969e+09 6.318297e+08 1.794525e+09 0.0 0.0 5.414536e+08 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 3.919551e+09 1.445189e+10 0.0 2.391377e+06 1.445429e+10
2001 2.191320e+09 1.349368e+09 2.332168e+08 6.378740e+08 0.0 0.0 2.025203e+08 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 1.426169e+09 5.271991e+09 0.0 8.790758e+05 5.272870e+09
2002 2.698970e+09 1.657363e+09 2.845645e+08 7.803337e+08 0.0 0.0 2.361252e+08 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 1.772188e+09 6.533002e+09 0.0 1.081372e+06 6.534083e+09
2003 4.231349e+09 2.545516e+09 4.447166e+08 1.210860e+09 0.0 0.0 3.712389e+08 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 2.775669e+09 1.019997e+10 0.0 1.727943e+06 1.020169e+10
2004 8.532618e+09 4.972363e+09 9.090725e+08 2.421317e+09 0.0 0.0 7.039480e+08 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 5.751251e+09 2.120316e+10 0.0 3.626892e+06 2.120679e+10
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2096 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.0 0.0 0.000000e+00 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.000000e+00 0.000000e+00 0.0 0.000000e+00 0.000000e+00
2097 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.0 0.0 0.000000e+00 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.000000e+00 0.000000e+00 0.0 0.000000e+00 0.000000e+00
2098 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.0 0.0 0.000000e+00 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.000000e+00 0.000000e+00 0.0 0.000000e+00 0.000000e+00
2099 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.0 0.0 0.000000e+00 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.000000e+00 0.000000e+00 0.0 0.000000e+00 0.000000e+00
2100 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.0 0.0 0.000000e+00 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.000000e+00 0.000000e+00 0.0 0.000000e+00 0.000000e+00

101 rows × 3809 columns

Cabon Emissions by material or module¶

In [19]:
for scen in scenarios:

    scen_annual_carbon = sim_annual_carbon.filter(like='Annual_Emit').filter(like=scen)/1e12 #million tonnes
    
    plt.plot([],[],color=colormats[0], label=MATERIALS[0])
    plt.plot([],[],color=colormats[1], label=MATERIALS[1])
    plt.plot([],[],color=colormats[2], label=MATERIALS[2])
    plt.plot([],[],color=colormats[3], label=MATERIALS[3])
    plt.plot([],[],color=colormats[4], label=MATERIALS[4])
    plt.plot([],[],color=colormats[5], label=MATERIALS[5])
    plt.plot([],[],color=colormats[6], label=MATERIALS[6])
    plt.plot([],[],color=colormats[7], label='module')


    plt.stackplot(scen_annual_carbon.index,
                  scen_annual_carbon[scen+'_Annual_Emit_glass_gCO2eq'], 
                  scen_annual_carbon[scen+'_Annual_Emit_silicon_gCO2eq'],
                  scen_annual_carbon[scen+'_Annual_Emit_silver_gCO2eq'], 
                  scen_annual_carbon[scen+'_Annual_Emit_aluminium_frames_gCO2eq'], 
                  scen_annual_carbon[scen+'_Annual_Emit_copper_gCO2eq'],
                  scen_annual_carbon[scen+'_Annual_Emit_encapsulant_gCO2eq'],
                  scen_annual_carbon[scen+'_Annual_Emit_backsheet_gCO2eq'],
                  scen_annual_carbon[scen+'_Annual_Emit_mod_gCO2eq'],
                  colors = colormats)
    plt.title(scen+':\nGHG Emissions Annually by Module and Material Lifecycle')
    plt.ylabel('GHG Emissions Annually from Lifecycle Mats and Mods\n[million metric tonnes CO2eq]')
    plt.xlim(2000,2100)

    handles, labels = plt.gca().get_legend_handles_labels()
#specify order of items in legend
#order = [1,2,0]
#add legend to plot
#plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order])
    plt.legend(handles[::-1], labels[::-1], bbox_to_anchor=(1.4,1))

#plt.legend()
    plt.show()
In [20]:
sim_cumu_carbon = sim_annual_carbon.cumsum()
maxy = round(sim_cumu_carbon.loc[2100].filter(like='Annual_Emit_total_modmats').max()/1e12,-3)
sim_cumu_carbon.loc[2100].filter(like='Annual_Emit_total_modmats')
Out[20]:
PV_ICE_Annual_Emit_total_modmats_gCO2eq               2.963565e+16
r_PERC_Annual_Emit_total_modmats_gCO2eq               2.126845e+16
r_SHJ_Annual_Emit_total_modmats_gCO2eq                2.024885e+16
r_TOPCon_Annual_Emit_total_modmats_gCO2eq             2.067584e+16
r_IRENA_Annual_Emit_total_modmats_gCO2eq              3.050720e+16
ex_Life_Annual_Emit_total_modmats_gCO2eq              2.274670e+16
ex_High_eff_Annual_Emit_total_modmats_gCO2eq          3.053505e+16
ex_Circular_Annual_Emit_total_modmats_gCO2eq          2.721381e+16
h_EffLife_Annual_Emit_total_modmats_gCO2eq            2.295700e+16
h_50PERC_Annual_Emit_total_modmats_gCO2eq             2.264909e+16
h_RecycledPERC_Annual_Emit_total_modmats_gCO2eq       2.254946e+16
h_Perovskite_life_Annual_Emit_total_modmats_gCO2eq    2.653388e+16
h_Perovskite_Eff_Annual_Emit_total_modmats_gCO2eq     2.293411e+16
Name: 2100, dtype: float64
In [21]:
#colormats = ['#00bfbf','#ff7f0e','#1f77be','#2ca02c','#d62728','#9467BD','#8C564B','black'] #colors for material plots
for scen in scenarios:

    scen_cumu_carbon = sim_cumu_carbon.filter(like='Annual_Emit').filter(like=scen)/1e12 #million tonnes
    
    plt.plot([],[],color=colormats[0], label=MATERIALS[0])
    plt.plot([],[],color=colormats[1], label=MATERIALS[1])
    plt.plot([],[],color=colormats[2], label=MATERIALS[2])
    plt.plot([],[],color=colormats[3], label=MATERIALS[3])
    plt.plot([],[],color=colormats[4], label=MATERIALS[4])
    plt.plot([],[],color=colormats[5], label=MATERIALS[5])
    plt.plot([],[],color=colormats[6], label=MATERIALS[6])
    plt.plot([],[],color=colormats[7], label='module')


    plt.stackplot(scen_cumu_carbon.index,
                  scen_cumu_carbon[scen+'_Annual_Emit_glass_gCO2eq'], 
                  scen_cumu_carbon[scen+'_Annual_Emit_silicon_gCO2eq'],
                  scen_cumu_carbon[scen+'_Annual_Emit_silver_gCO2eq'], 
                  scen_cumu_carbon[scen+'_Annual_Emit_aluminium_frames_gCO2eq'], 
                  scen_cumu_carbon[scen+'_Annual_Emit_copper_gCO2eq'],
                  scen_cumu_carbon[scen+'_Annual_Emit_encapsulant_gCO2eq'],
                  scen_cumu_carbon[scen+'_Annual_Emit_backsheet_gCO2eq'],
                  scen_cumu_carbon[scen+'_Annual_Emit_mod_gCO2eq'],
                  colors = colormats)
    plt.title(scen+':\nGHG Emissions Annually by Module and Material Lifecycle')
    plt.ylabel('GHG Emissions Annually from Lifecycle Mats and Mods\n[million metric tonnes CO2eq]')
    plt.xlim(2000,2100)
    plt.ylim(0,maxy)

    handles, labels = plt.gca().get_legend_handles_labels()
#specify order of items in legend
#order = [1,2,0]
#add legend to plot
#plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order])
    plt.legend(handles[::-1], labels[::-1], bbox_to_anchor=(1.4,1))

#plt.legend()
    plt.show()
In [22]:
sim_cumu_carbon
Out[22]:
PV_ICE_Annual_Emit_mod_gCO2eq PV_ICE_Annual_Emit_glass_gCO2eq PV_ICE_Annual_Emit_silicon_gCO2eq PV_ICE_Annual_Emit_silver_gCO2eq PV_ICE_Annual_Emit_aluminium_frames_gCO2eq PV_ICE_Annual_Emit_copper_gCO2eq PV_ICE_Annual_Emit_encapsulant_gCO2eq PV_ICE_Annual_Emit_backsheet_gCO2eq PV_ICE_Annual_Emit_total_modmats_gCO2eq r_PERC_Annual_Emit_mod_gCO2eq ... h_Perovskite_life_Annual_Emit_total_modmats_gCO2eq h_Perovskite_Eff_Annual_Emit_mod_gCO2eq h_Perovskite_Eff_Annual_Emit_glass_gCO2eq h_Perovskite_Eff_Annual_Emit_silicon_gCO2eq h_Perovskite_Eff_Annual_Emit_silver_gCO2eq h_Perovskite_Eff_Annual_Emit_aluminium_frames_gCO2eq h_Perovskite_Eff_Annual_Emit_copper_gCO2eq h_Perovskite_Eff_Annual_Emit_encapsulant_gCO2eq h_Perovskite_Eff_Annual_Emit_backsheet_gCO2eq h_Perovskite_Eff_Annual_Emit_total_modmats_gCO2eq
2000 1.302635e+10 7.926511e+10 9.851568e+11 1.806152e+10 2.951140e+11 1.095485e+08 3.144897e+10 1.445429e+10 1.436637e+12 1.132197e+10 ... 1.436637e+12 1.302635e+10 7.926511e+10 9.851568e+11 1.806152e+10 2.951140e+11 1.095485e+08 3.144897e+10 1.445429e+10 1.436637e+12
2001 1.765591e+10 1.071198e+11 1.297904e+12 2.408112e+10 3.999794e+11 1.466912e+08 4.262252e+10 1.972716e+10 1.909237e+12 1.648577e+10 ... 1.909237e+12 1.765591e+10 1.071198e+11 1.297904e+12 2.408112e+10 3.999794e+11 1.466912e+08 4.262252e+10 1.972716e+10 1.909237e+12
2002 2.333327e+10 1.403331e+11 1.611551e+12 3.098442e+10 5.248225e+11 1.912913e+08 5.610222e+10 2.626124e+10 2.413579e+12 2.179171e+10 ... 2.413579e+12 2.333327e+10 1.403331e+11 1.611551e+12 3.098442e+10 5.248225e+11 1.912913e+08 5.610222e+10 2.626124e+10 2.413579e+12
2003 3.216826e+10 1.903309e+11 2.003080e+12 4.102774e+10 7.171775e+11 2.602262e+08 7.661591e+10 3.646293e+10 3.097123e+12 3.004872e+10 ... 3.097123e+12 3.216826e+10 1.903309e+11 2.003080e+12 4.102774e+10 7.171775e+11 2.602262e+08 7.661591e+10 3.646293e+10 3.097123e+12
2004 4.977404e+10 2.906310e+11 2.656899e+12 6.048733e+10 1.094709e+12 4.034545e+08 1.182876e+11 5.766972e+10 4.328861e+12 4.650269e+10 ... 4.328861e+12 4.977404e+10 2.906310e+11 2.656899e+12 6.048733e+10 1.094709e+12 4.034545e+08 1.182876e+11 5.766972e+10 4.328861e+12
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2096 8.661511e+13 9.681365e+15 6.186923e+15 3.830767e+13 7.434280e+15 8.232011e+12 2.882390e+15 2.116173e+15 2.843429e+16 7.955485e+13 ... 2.588437e+16 1.055897e+14 6.725123e+15 5.651124e+15 6.575802e+13 4.077729e+15 1.502691e+13 5.546502e+15 2.325043e+13 2.221010e+16
2097 8.661511e+13 9.782482e+15 6.233863e+15 3.859118e+13 7.507335e+15 8.305166e+12 2.912165e+15 2.137900e+15 2.870726e+16 7.955485e+13 ... 2.604102e+16 1.055897e+14 6.765008e+15 5.690372e+15 6.651118e+13 4.099235e+15 1.522125e+13 5.625601e+15 2.325043e+13 2.239079e+16
2098 8.661511e+13 9.890074e+15 6.283796e+15 3.889277e+13 7.585002e+15 8.382985e+12 2.943838e+15 2.161013e+15 2.899762e+16 7.955485e+13 ... 2.619843e+16 1.055897e+14 6.803572e+15 5.730015e+15 6.728376e+13 4.119829e+15 1.542060e+13 5.706738e+15 2.325043e+13 2.257170e+16
2099 8.661511e+13 1.000423e+16 6.336764e+15 3.921269e+13 7.667346e+15 8.465533e+12 2.977437e+15 2.185530e+15 2.930560e+16 7.955485e+13 ... 2.635667e+16 1.055897e+14 6.840671e+15 5.770083e+15 6.807751e+13 4.139413e+15 1.562540e+13 5.790099e+15 2.325043e+13 2.275281e+16
2100 8.661511e+13 1.012661e+16 6.393529e+15 3.955555e+13 7.755542e+15 8.553999e+12 3.013444e+15 2.211805e+15 2.963565e+16 7.955485e+13 ... 2.653388e+16 1.055897e+14 6.876225e+15 5.810594e+15 6.889341e+13 4.157934e+15 1.583593e+13 5.875786e+15 2.325043e+13 2.293411e+16

101 rows × 117 columns

In [23]:
#create a df from which to do a bar chart of 2100 emissions by mat/mod
mats_emit_2100 = pd.DataFrame() #index=scennames_labels_flat
for mat in MATERIALS:
    mat_emit_2100 = pd.Series(sim_cumu_carbon.loc[2100].filter(like=mat).values)
    mats_emit_2100 = pd.concat([mats_emit_2100, mat_emit_2100], axis=1)

mats_emit_2100
mats_emit_2100.columns = MATERIALS
modmats_emit_2100 = pd.concat([mats_emit_2100,pd.Series(sim_cumu_carbon.loc[2100].filter(like='mod_').values)], axis=1)
modmats_emit_2100.index = scennames_labels_flat
modmats_emit_2100.rename(columns={0:'module'}, inplace=True)
modmats_emit_2100_megatonne = modmats_emit_2100/1e12
modmats_emit_2100_megatonne
Out[23]:
glass silicon silver aluminium_frames copper encapsulant backsheet module
PV_ICE 10126.610037 6393.528822 39.555545 7755.542240 8.553999 3013.443849 2211.805161 86.615111
PERC 8762.994612 4692.866948 25.191311 4878.062693 7.933845 2800.113453 21.729228 79.554845
SHJ 8331.662313 4465.729982 44.794737 4639.401770 7.545504 2662.628153 21.330504 75.758903
TOPCon 8509.280286 4559.621644 43.333633 4737.762881 7.705695 2719.277076 21.528020 77.335447
Low Quality 10920.089504 4341.113961 40.635431 6893.787669 12.970040 4731.060652 3466.044108 101.496270
Long-Lived 9270.300486 5078.154692 29.471217 6013.561653 6.784228 2244.838020 22.463092 81.124534
High Eff 12963.265245 6145.955317 38.732726 8176.783647 8.755677 3115.456836 21.291503 64.810392
Circular 8977.849914 4281.988316 92.251404 5569.326850 21.597082 8123.698405 23.250434 123.842906
High Eff + Long-life 9507.951929 4926.966042 31.290604 6098.983123 6.741955 2295.131175 21.291435 68.646424
Long-Life + Recycling 9400.037004 5075.691113 32.719877 5668.840797 6.882528 2360.827092 22.463337 81.633002
Recycled-Si + Long-life 8218.255683 2257.178941 33.593199 4716.342625 11.277104 4044.829561 2965.494878 302.484496
Circular + Long-life 8836.160604 6431.136887 66.818549 5517.225262 15.130176 5526.821345 23.250434 117.337425
Circular + High Eff 6876.225455 5810.593695 68.893415 4157.934259 15.835931 5875.785931 23.250434 105.589706
In [24]:
fig_cumuemit_modmat, (ax0,ax2,ax3) = plt.subplots(1,3,figsize=(15,8), sharey=True, 
                                      gridspec_kw={'wspace': 0, 'width_ratios': [1.5,1,1.5]})
#BAU
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['glass'], color=colormats[0])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['silicon'],
        bottom=modmats_emit_2100_megatonne[0:5]['glass'], color=colormats[1])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['silver'],
       bottom=modmats_emit_2100_megatonne.iloc[0:5,0:2].sum(axis=1), color=colormats[2])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['aluminium_frames'],
       bottom=modmats_emit_2100_megatonne.iloc[0:5,0:3].sum(axis=1), color=colormats[3])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['copper'],
       bottom=modmats_emit_2100_megatonne.iloc[0:5,0:4].sum(axis=1), color=colormats[4])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['encapsulant'],
       bottom=modmats_emit_2100_megatonne.iloc[0:5,0:5].sum(axis=1), color=colormats[5])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['backsheet'],
       bottom=modmats_emit_2100_megatonne.iloc[0:5,0:6].sum(axis=1), color=colormats[6])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['module'],
       bottom=modmats_emit_2100_megatonne.iloc[0:5,0:7].sum(axis=1), color='black')

ax0.set_ylim(0,31000)
ax0.set_ylabel('Cumulative Carbon Emissions\n[million metric tonnes CO2eq]', fontsize=20)
ax0.set_title('Baseline', fontsize=14)
ax0.set_xticklabels(labels=scennames_labels[0:5], rotation=45)
ax0.grid(axis='y', color='0.9', ls='--') 
ax0.set_axisbelow(True)

#Extreme
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['glass'], color=colormats[0])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['silicon'],
        bottom=modmats_emit_2100_megatonne[5:8]['glass'], color=colormats[1])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['silver'],
       bottom=modmats_emit_2100_megatonne.iloc[5:8,0:2].sum(axis=1), color=colormats[2])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['aluminium_frames'],
       bottom=modmats_emit_2100_megatonne.iloc[5:8,0:3].sum(axis=1), color=colormats[3])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['copper'],
       bottom=modmats_emit_2100_megatonne.iloc[5:8,0:4].sum(axis=1), color=colormats[4])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['encapsulant'],
       bottom=modmats_emit_2100_megatonne.iloc[5:8,0:5].sum(axis=1), color=colormats[5])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['backsheet'],
       bottom=modmats_emit_2100_megatonne.iloc[5:8,0:6].sum(axis=1), color=colormats[6])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['module'],
       bottom=modmats_emit_2100_megatonne.iloc[5:8,0:7].sum(axis=1), color='black')

ax2.set_title('Extreme', fontsize=14)
ax2.set_xticklabels(labels=scennames_labels[5:8], rotation=45)
ax2.grid(axis='y', color='0.9', ls='--') 
ax2.set_axisbelow(True)

#Ambitious
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['glass'], color=colormats[0])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['silicon'],
        bottom=modmats_emit_2100_megatonne[8:]['glass'], color=colormats[1])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['silver'],
       bottom=modmats_emit_2100_megatonne.iloc[8:,0:2].sum(axis=1), color=colormats[2])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['aluminium_frames'],
       bottom=modmats_emit_2100_megatonne.iloc[8:,0:3].sum(axis=1), color=colormats[3])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['copper'],
       bottom=modmats_emit_2100_megatonne.iloc[8:,0:4].sum(axis=1), color=colormats[4])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['encapsulant'],
       bottom=modmats_emit_2100_megatonne.iloc[8:,0:5].sum(axis=1), color=colormats[5])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['backsheet'],
       bottom=modmats_emit_2100_megatonne.iloc[8:,0:6].sum(axis=1), color=colormats[6])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['module'],
       bottom=modmats_emit_2100_megatonne.iloc[8:,0:7].sum(axis=1), color='black')


ax3.set_title('Ambitious', fontsize=14)
ax3.set_xticklabels(labels=scennames_labels[8:], rotation=45)
ax3.grid(axis='y', color='0.9', ls='--') 
ax3.set_axisbelow(True)

#overall fig

fig_cumuemit_modmat.suptitle('Cumulative Emisisons in 2100 by material', fontsize=24)
plt.show()

#fig_cumuemit_modmat.savefig('energyresults-energyBalance.png', dpi=300, bbox_inches='tight')

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_10592\3009395773.py:23: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax0.set_xticklabels(labels=scennames_labels[0:5], rotation=45)
C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_10592\3009395773.py:45: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(labels=scennames_labels[5:8], rotation=45)
C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_10592\3009395773.py:68: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax3.set_xticklabels(labels=scennames_labels[8:], rotation=45)

Cumulative Carbon in 2050 and 2100¶

In [25]:
#mins in 2050 and 2100
cumu_carbon_2050 = sim_cumu_carbon.loc[2050].filter(like='Annual_Emit_total_modmats')/1e12
cumu_carbon_2100 = sim_cumu_carbon.loc[2100].filter(like='Annual_Emit_total_modmats')/1e12
cumu_carbon_rankings_crittime = pd.concat([cumu_carbon_2050,cumu_carbon_2100], axis=1)
cumu_carbon_rankings_crittime.index = scennames_labels_flat
cumu_carbon_rankings_crittime
Out[25]:
2050 2100
PV_ICE 14058.924952 29635.654765
PERC 12794.376877 21268.446936
SHJ 12187.971284 20248.851865
TOPCon 12443.042440 20675.844681
Low Quality 15135.691476 30507.197634
Long-Lived 13425.138619 22746.697922
High Eff 10927.903114 30535.051343
Circular 17562.405463 27213.805311
High Eff + Long-life 11214.153373 22957.002686
Long-Life + Recycling 13438.818720 22649.094749
Recycled-Si + Long-life 14238.914154 22549.456487
Circular + Long-life 18287.841124 26533.880681
Circular + High Eff 15025.479472 22934.108827
In [26]:
cumu_carbon_rankings_crittime_plot = cumu_carbon_rankings_crittime.copy()
cumu_carbon_rankings_crittime_plot['diff'] = cumu_carbon_rankings_crittime[2100]-cumu_carbon_rankings_crittime[2050]
In [27]:
fig_cumulativeemit, (ax0,ax2,ax3) = plt.subplots(1,3,figsize=(15,8), sharey=True, 
                                      gridspec_kw={'wspace': 0, 'width_ratios': [1.5,1,1.5]})
#BAU
ax0.bar(cumu_carbon_rankings_crittime_plot.index[0:5], cumu_carbon_rankings_crittime_plot[2050].iloc[0:5],
        tick_label=scennames_labels[0:5], color=colorpalette[0:5], alpha = 0.7, edgecolor='white')
ax0.bar(cumu_carbon_rankings_crittime_plot.index[0:5], cumu_carbon_rankings_crittime_plot['diff'].iloc[0:5],
        bottom=cumu_carbon_rankings_crittime_plot[2050].iloc[0:5],
        tick_label=scennames_labels[0:5], color=colorpalette[0:5])
ax0.set_ylim(0,31000)
ax0.set_ylabel('Cumulative Carbon Emissions\n[million metric tonnes CO2eq]', fontsize=20)
ax0.set_title('Baseline', fontsize=14)
ax0.set_xticklabels(labels=scennames_labels[0:5], rotation=45)
ax0.grid(axis='y', color='0.9', ls='--') 
ax0.set_axisbelow(True)

#Extreme
ax2.bar(cumu_carbon_rankings_crittime_plot.index[5:8], cumu_carbon_rankings_crittime_plot[2050].iloc[5:8],
        tick_label=scennames_labels[5:8], color=colorpalette[5:8], alpha = 0.7, edgecolor='white')
ax2.bar(cumu_carbon_rankings_crittime_plot.index[5:8], cumu_carbon_rankings_crittime_plot['diff'].iloc[5:8],
        bottom=cumu_carbon_rankings_crittime_plot[2050].iloc[5:8],
        tick_label=scennames_labels[5:8], color=colorpalette[5:8])
ax2.set_title('Extreme', fontsize=14)
ax2.set_xticklabels(labels=scennames_labels[5:8], rotation=45)
ax2.grid(axis='y', color='0.9', ls='--') 
ax2.set_axisbelow(True)

#Ambitious
ax3.bar(cumu_carbon_rankings_crittime_plot.index[8:], cumu_carbon_rankings_crittime_plot[2050].iloc[8:],
        tick_label=scennames_labels[8:], color=colorpalette[8:], hatch='x', edgecolor='white', alpha=0.7)
ax3.bar(cumu_carbon_rankings_crittime_plot.index[8:], cumu_carbon_rankings_crittime_plot['diff'].iloc[8:],
        bottom=cumu_carbon_rankings_crittime_plot[2050].iloc[8:],
        tick_label=scennames_labels[8:], color=colorpalette[8:], hatch='x', edgecolor='white')
ax3.set_title('Ambitious', fontsize=14)
ax3.set_xticklabels(labels=scennames_labels[8:], rotation=45)
ax3.grid(axis='y', color='0.9', ls='--') 
ax3.set_axisbelow(True)

#overall fig

fig_cumulativeemit.suptitle('Cumulative Emisisons in 2050, 2100', fontsize=24)
plt.show()

#fig_eBalance.savefig('energyresults-energyBalance.png', dpi=300, bbox_inches='tight')

¶

Emissions by electricity vs fuels vs process¶

Process emission summing¶

This only happens on the material files

In [120]:
process_emissions = pd.DataFrame()
for scen in scenarios:
    scen_p_totrim = sim_carbon_results.filter(like=scen).filter(like='_p_')
    exclude_p_sums = ['_HQ_p_','_LQ_p_','_e_p_']
    exclude_p_sums_search = '|'.join(exclude_p_sums)
    scen_p = scen_p_totrim.loc[:,~scen_p_totrim.columns.str.contains(exclude_p_sums_search)]
    scen_p_sum = scen_p.sum(axis=1)
    process_emissions = pd.concat([process_emissions,scen_p_sum], axis=1)

process_emissions.columns = scennames_labels_flat
process_emissions.index = pd.RangeIndex(start=2000,stop=2101,step=1)
process_emissions_cumu = process_emissions.cumsum()

Fuel Emissions¶

This is capturing steam and heating fuel

In [121]:
fuel_emissions = pd.DataFrame()
for scen in scenarios:
    scen_f = sim_carbon_results.filter(like=scen).filter(like='_fuel_')
    scen_f_sum = scen_f.sum(axis=1)
    fuel_emissions = pd.concat([fuel_emissions,scen_f_sum], axis=1)
    
fuel_emissions.columns = scennames_labels_flat
fuel_emissions.index = pd.RangeIndex(start=2000,stop=2101,step=1)
fuel_emissions_cumu = fuel_emissions.cumsum()

Electricity Emissions¶

both module and material level elec.

In [122]:
elec_emissions = pd.DataFrame()
for scen in scenarios:
    scen_mod_elec = sim_annual_carbon.filter(like=scen).filter(like='Annual_Emit_mod') #module elec lifecycle energy
    
    mat_eleckey = ['Global_vmfg_elec','landfill_elec','ReMFG_clean','Recycled_LQ','Recycled_HQ_elec']
    mat_elecs_search = '|'.join(mat_eleckey)
    scen_mat_elecs = sim_carbon_results.loc[:,sim_carbon_results.columns.str.contains(mat_elecs_search)].filter(like=scen)
    
    #sum them together by scen
    scen_mat_elecs_sum = scen_mat_elecs.sum(axis=1)
    scen_elec_modmat_annual_sum = scen_mat_elecs_sum+scen_mod_elec.iloc[:,0]
    elec_emissions = pd.concat([elec_emissions,scen_elec_modmat_annual_sum], axis=1)
    
elec_emissions.columns=scennames_labels_flat
elec_emissions.index = pd.RangeIndex(start=2000,stop=2101,step=1)
elec_emissions_cumu = elec_emissions.cumsum()
In [123]:
#(elec_emissions+fuel_emissions+process_emissions) vs sim_annual_carbon
#TO DO: I have a double counting or missing data problem somewhere, these should add to the same I think.
In [124]:
#graphing by emission source
efp_emit_total = elec_emissions+fuel_emissions+process_emissions
In [125]:
efp_emit_total.index
Out[125]:
RangeIndex(start=2000, stop=2101, step=1)
In [126]:
#graphing by emission source, annual
#efp_emit_total = elec_emissions+fuel_emissions+process_emissions

for scen in scennames_labels_flat:
    
    plt.plot([],[],color='black', label='process')
    plt.plot([],[],color='darkred', label='fuel')
    plt.plot([],[],color='blue', label='electricity')

    plt.stackplot(elec_emissions.index,
                  process_emissions[scen]/1e12, 
                  fuel_emissions[scen]/1e12,
                  elec_emissions[scen]/1e12, 
                  colors = ['black','darkred','blue'])
    plt.title(scen+':\nGHG Emissions Annually by Source')
    plt.ylabel('GHG Emissions Annually from Lifecycle Source\n[million metric tonnes CO2eq]')
    plt.xlim(2000,2100)
    plt.ylim(0,)

    handles, labels = plt.gca().get_legend_handles_labels()
#specify order of items in legend
#order = [1,2,0]
#add legend to plot
#plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order])
    plt.legend(handles[::-1], labels[::-1], bbox_to_anchor=(1.4,1))

#plt.legend()
    plt.show()
In [128]:
#graphing by emission source, annual

for scen in scennames_labels_flat:
    
    plt.plot([],[],color='black', label='process')
    plt.plot([],[],color='darkred', label='fuel')
    plt.plot([],[],color='blue', label='electricity')

    plt.stackplot(elec_emissions_cumu.index,
                  process_emissions_cumu[scen]/1e12, 
                  fuel_emissions_cumu[scen]/1e12,
                  elec_emissions_cumu[scen]/1e12, 
                  colors = ['black','darkred','blue'])
    plt.title(scen+':\nGHG Emissions Annually by Source')
    plt.ylabel('GHG Emissions Annually from Lifecycle Source\n[million metric tonnes CO2eq]')
    plt.xlim(2000,2100)
    plt.ylim(0,maxy)

    handles, labels = plt.gca().get_legend_handles_labels()
#specify order of items in legend
#order = [1,2,0]
#add legend to plot
#plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order])
    plt.legend(handles[::-1], labels[::-1], bbox_to_anchor=(1.4,1))

#plt.legend()
    plt.show()
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